GenAI success demands employee training that goes beyond certifications

In HFS’ recent Horizons study – Generative Enterprise Services, 2025, we interviewed 130 employees from 40 leading AI / GenAI service providers covered in the report. The findings reveal that more than 80% of employees trained in GenAI still feel unprepared to use it effectively. As organizations pour money into AI certifications, they realize too late that a badge doesn’t equal business readiness.

While enterprises, service providers, and tech partners are investing vast resources in training their employees on AI / GenAI technologies, most of it is theoretical and one-size-fits-all. Without a reset in how organizations develop GenAI fluency, they risk turning their AI investments into partially functioning assets.

Organizations must foster environments for real-time human and AI collaboration

As organizations embrace GenAI, the AI skillset isn’t just about prompt engineering or deploying foundation models. It’s about cultivating a workforce that can ask the right questions, interpret machine insights with human judgment, and operate in environments where humans and AI collaborate in real-time. The key skills evolving include problem-solving, adaptive learning, and critical thinking (see Exhibit 1).

Exhibit 1: Problem solving and adaptability top the list of essential-yet-lacking skills; development skills and prompt engineering prop up the bottom of this table

Source: Sample: 104 enterprise leaders actively exploring and deploying GenAI
Source: HFS Research in partnership with Ascendion, 2023

The integration of AI necessitates a technically proficient workforce that is adept at collaborating with advanced AI systems. Additionally, emotional intelligence and leadership skills are crucial to managing teams where humans and AI coexist, ensuring seamless integration and harnessing the strengths of both.

Exhibit 2: Both IT and business roles still feel dissatisfied with the current structured training programs and highlight further training opportunities

Sample: 130 employees of key 40 service providers covered in HFS Horizons: Generative Enterprise Services, 2025 (n = 92, IT role; n = 38, Business role;)
Source: HFS Horizons: Generative Enterprise Services, 2025

The HFS Horizons: Generative Enterprise Services, 2025 study reveals how deep the disconnect runs. While 98% of the respondents reported receiving formal training in GenAI, 82% still felt unprepared and needed further upskilling. Organizations are aggressively lapping up training offered by hyperscalers such as AWS, Microsoft, and Google on their technologies and other certified training courses to increase the number of certifications. However, certifications alone are not enough.

This gap highlights the need for holistic, interdisciplinary training programs that blend technical, ethical, strategic, and communication skills. Training should encompass technology and its impact, organizational strategy with aligned learning goals, and leadership support, allowing flexibility in accommodating different learning curves.

While our research focused on service provider employees, the patterns we uncovered should serve as a wake-up call for all enterprise leaders building similar GenAI training programs. If providers—arguably ahead in GenAI maturity—face readiness gaps, enterprise training strategies will likely face even greater challenges.

Different roles, different gaps: Training must reflect the roles—not just the tools

The gap isn’t just about content; it’s about context. Different roles demand fundamentally different learning models (see Exhibit 2):

• Technical and operational employees want more practical and hands-on environments. They need access to real datasets, challenges, and real-time feedback to fine-tune and deploy GenAI models.
• Strategic and leadership roles require much more than tool knowledge. They need guidance on governance, ethics, and business alignment—plus the communication skills to drive AI adoption across non-technical teams and improved change management.
• Both groups demand more interaction with hyperscalers, domain experts, and internal peer communities beyond certification courses. Also, both feel the need for ongoing training to stay aligned with the latest innovations, stressing the importance of staying updated on rapidly evolving AI technologies and trends.

Most organizations still miss the real differentiator—building a team that is the right combination of artists and scientists

The current training programs are too tech-focused and don’t give enough importance to artistic skills such as creative problem-solving or critical thinking. Successful AI usage comes from combining rigorous analysis with unconventional thinking. We need more artists who can imagine different questions, approach problems creatively, and challenge machine-drawn conclusions. And we still need scientists who interrogate data, structure logic, and engineer reliable systems.

Organizations need both kinds of employees— STEM (science-technology-engineering-math) and liberal arts-oriented. The gap lies in liberal arts—folks who have the imagination to wonder what AI could do next. Recognizing that one person may not embody these combined skills is important. Therefore, it’s essential to build teams that combine these skills.

Currently, the training programs, assessment processes, recruitment system, and AI-led talent filtering are slanted toward STEM mindsets. These must be changed so that the talent net is widened to train existing employees in liberal arts skills and bring in new skills that all organizations sorely need. Unless companies address this, they will keep deepening their skills deficit—missing out on the talent that would otherwise imagine and challenge the organization’s way to the future.

The Bottom Line: Leaders must revamp AI training to be immersive, role-based, and adaptive, encouraging experimentation, critical thinking, and business alignment while blending artistic and scientific talent.

Enterprise leaders must stop confusing certification with capability. If 80% of trained employees still feel dissatisfied, the message is clear: today’s AI training models are ineffective. Most organizations need the right combination of artists and scientists to drive holistic thinking that involves solving problems in unconventional ways. The focus of training must shift toward continuous learning that encourages real-world experimentation, sharpens critical thinking, and aligns directly with business outcomes. Without this shift, even the most ambitious GenAI strategies will fail to move to enterprise-wide production.